Short Report: A simple model to predict the potential abundance
of Aedes aegypti mosquitoes a month in advance
Running Head: A simple model for Aedes aegypti
Authors: Andrew J. Monaghan1*, Mary H. Hayden1, Kirk A. Smith2,
M.H. Reiskind3, Ryan Cabell1, and Kacey C. Ernst4
1National Center for Atmospheric Research, Boulder, CO, USA
2Maricopa County Environmental Services Vector Control Division,
Phoenix, AZ, USA
3Department of Entomology, North Carolina State University,
Raleigh, NC, USA
4University of Arizona, Mel and Enid Zuckerman College of Public
Health, Tucson, AZ, USA
Key words: Aedes aegypti, seasonality, model, prediction,
climate
Word count abstract: 150 words max
Word count text: 1500 words max
Number of figures: 2
Number of tables: 1
Supplemental material: Supplemental Figure 1, Supplemental Text
1
* PO Box 3000, Boulder, CO 80307 (USA). [email protected].
303.497.8424
Abstract
The mosquito Aedes (Stegomyia) aegypti (L.) is the primary
vector of dengue, chikungunya and Zika viruses in the United
States. Surveillance for Ae. aegypti is limited, hindering
understanding of the mosquitos seasonal patterns and predictions of
elevated risk for autochthonous virus transmission. We developed a
simple and intuitive empirical model that employs readily-available
temperature and humidity variables to predict environmental
suitability for low, medium or high abundance of adult Ae. aegypti
in a given city one month in advance. The model correctly predicted
the potential abundance of Ae. aegypti in >70% of months in arid
Phoenix, Arizona (over a 10- year period) and humid Miami, Florida
(over a 2-year period). The monthly model predictions can be
updated daily, weekly or monthly, and thus may be applied to
forecast suitable conditions for Ae. aegypti to inform
vector-control activities and guide household-level actions to
reduce habitat and human exposure.
Main Text
The mosquito Aedes (Stegomyia) aegypti (L.) is the primary
vector of dengue, chikungunya and Zika viruses in the United
States.(Grubaugh et al. 2017) Surveillance for Ae. aegypti is
limited: presence of the species has only been recorded in 291
counties of the 1,443-2,209 counties deemed environmentally
suitable.(Johnson et al. 2017) Most of these records only note the
presence of the mosquito a few times over the past several
decades.(Hahn et al. 2016) Even fewer temporal records of Ae.
aegypti presence or abundance exist, hindering understanding of the
seasonal patterns of the mosquito, and the ability to predict
periods of elevated risk for autochthonous virus
transmission.(Monaghan et al. 2016)
The seasonal presence and abundance of Ae. aegypti in the U.S.
is limited by cool and/or dry meteorological conditions.(Eisen and
Moore 2013) With many of the weather-driven bionomics of Ae.
aegypti known(Morin et al. 2013), it may be possible to address
surveillance gaps using weather-driven dynamic mosquito simulation
models(Focks et al. 1993; Morin et al. 2015) to estimate the
seasonality of Ae. aegypti abundance across geographic
locations.(Monaghan et al. 2016) However, dynamic models are
challenging to implement due to computational expense, required
expertise, and uncertainty in model parameters(Xu et al. 2010).
Here, we describe a simple and intuitive empirical model that
employs readily-available temperature and humidity variables to
predict environmental suitability for low, medium or high abundance
of adult Ae. aegypti in a given city one month in advance. The
model may be used to quickly and easily forecast suitable
conditions for Ae. aegypti to inform public health and
vector-control activities, and guide resident actions to reduce
vector habitat and human exposure.
Two available temporal Ae. aegypti abundance records were used
for model fitting and evaluation. A 10-year (January 2006-December
2015) surveillance record of monthly adult abundance from ~750 CDC
light traps across Maricopa County (Phoenix, AZ) was collected by
co-author KAS and colleagues. A 2-year (June 2006-June 2008) record
of monthly egg abundance from 30 ovitraps across a 650 km2 area of
Palm Beach County (near Miami, FL) was collected by co-author MHR
and colleagues.(Reiskind and Lounibos 2013) The Phoenix record is
longer and draws on more traps, and thus was used for model
fitting. The Miami record is shorter, draws on fewer traps, and
measures egg rather than adult abundance, and thus was used for
model evaluation. Both records consist of aggregated counts across
all traps by city-month. The trap records have uncertainty due to
issues with species specificity, malfunctions and catch
loss(Sukumaran 2016), and are not directly comparable across
locations. We thus focused on relative abundance across seasons for
each city, normalizing the records by setting the maximum monthly
aggregate trap count in each record to 1,000, and proportionally
rescaling all other monthly counts. Next, base 10 logs of the
monthly Ae. aegypti counts (log(aegypti)) were computed because the
log-transformed values have linear relationships with the
meteorological variables. The logs of values between 1-1,000
conveniently vary between 0-3, allowing categorization of results
into low (0-1), medium (1-2) and high (2-3) potential abundance
categories.
Daily meteorological fields were obtained from version 2 of the
1/8th degree North American Land Data Assimilation System (NLDAS)
forcing dataset.(Cosgrove et al. 2003) Time series of rainfall,
relative humidity, vapor pressure, and temperature (minimum,
maximum and mean) were extracted for the coordinates of Phoenix and
Miami for 2006-2015 using bilinear interpolation. Temperature,
humidity and rainfall variables were selected because of their
association with Ae. aegypti suitability in previous models.(Focks
et al. 1993) Next, for each month for which log(aegypti) was to be
predicted, the 30-day mean of each meteorological variable from the
prior month was computed, allowing for a lag of 3 days. For
example, to predict log(aegypti) for a month beginning on 1 July,
one would compute the 30-day average meteorological fields from 29
May 27 June.This approach accounts for a ~3-day lag in the
availability of the NLDAS fields, enabling users of NLDAS to issue
a monthly forecast for log(aegypti) on the same day the model
prediction is made, rather than 3 days later. Note that the model
fit is insensitive to a 3-day versus 0-day lag, so users could
simply use monthly average meteorological variables from, e.g.,
June to predict July. The choice of 30-day/monthly average periods
to predict log(aegypti) approximates the duration of the combined
immature life stages and adult gonotropic cycle.(Focks et al.
1993)Comment by Ernst, Kacey C - (kernst): With rainfall, I wonder
if number of days with rainfall is actually better than average
rainfall. How correlated are those two? Or perhaps number of weeks
during the month with rainfall? The consistent habitat will be key.
I dont know for sure but one big rainfall as compared to modest
rainfalls scattered throughout the month may be very different or
does your WATCHM modeling indicate that water presence is robust to
timing and frequency of rainfall events?Comment by Ernst, Kacey C -
(kernst): Note here if they could run the model more frequently to
get at a projected weekly level or bi-monthly level. I am guessing
you could.Comment by Ernst, Kacey C - (kernst): Did you run a
sensitivity analysis? If so, state.
SAll six meteorological fields were tested for linear fit by
regressing them on log(aegypti) for Phoenix, precipitation,
relative humidity, vapor pressure, average monthly minimum,
maximum, and average daily temperature. Precipitation and relative
humidity did not have statistically significant fits, likely due to
their less pronounced seasonality in Phoenix.. Vapor pressure (an
absolute measure of humidity) and all three temperature variables
had statistically significant linear fits (p0.97). Comment by
Ernst, Kacey C - (kernst): Yes, I think you may want to try a few
different permutations of rainfall in order to get an
association.
The best-fit MLR model for log(aegypti) included minimum
temperature (Tmin; oC) and vapor pressure (e; hPa) as explanatory
variables:
log(aegypti) = -0.53968 + 0.07041*Tmin + 0.03829*e[1]
Predicted log(aegypti) is termed potential abundance of Ae.
aegypti here because it is essentially an estimate of environmental
suitability for levels of Ae. aegypti abundance, rather than an
explicit prediction of abundance. The standard error of regression
is 0.40 and the model explains 74% of the variation in log(aegypti)
in Phoenix (Table 1). Observed and predicted year-to-year monthly
and 10-year average monthly variations are shown in Figure 1a and
1b. When the 10-year average monthly predictions are converted to
potential abundance categories (high, medium and low) they match
the observed categories in 11 of 12 months (Figure 1c). Over the
entire 10-year period (n=120; not shown), the predicted categories
match those observed in 75% of months, are lower in 16.7% of
months, and are higher in 8.3% of months. Comment by Ernst, Kacey C
- (kernst): Which month is out of spec? This could be important
particularly if it is the month out of spec for most of the 120
months (i.e. it always gets April wrong for example, this might
mean there is something about April that needs to be thought about
more carefully ie part of planting season but no rain so lots of
people are watering. Comment by Ernst, Kacey C - (kernst): More
consistently lower. As mentioned previous, if there is a pattern in
the months lower vs. higher this could be very useful.
The model predictions for Miami are also shown in Fig. 1. The
2-year average monthly predictions match the observed categories in
10 of 12 months (Figure 1f). Over the entire 2-year period (n=25;
not shown), the predicted categories match those observed in 72% of
months, are lower in 16% of months, and higher in 12% of months.
Given that Miami is a humid subtropical environment compared to the
arid setting of Phoenix, and the Miami mosquito data was not used
in model fitting, the fact that the Miami categorical predictions
nearly as accurate as for Phoenix suggests the model can be applied
more broadly across different environments.
The MLR model was thus used to explore the seasonality of Ae.
aegypti across the entire contiguous U.S. (Figure 2). The 10-year
average NLDAS meteorological fields described above were used as
model inputs. Because winter conditions limit the northernmost
range of Ae. aegypti(Eisen et al. 2014) which the MLR model does
not account for the results were masked using an
observation-constrained map of environmental suitability for Ae.
aegypti presence.(Johnson et al. 2017) The resulting maps indicate
areas of high potential abundance in the Southeast from
July-November. Potential abundance in south Florida and
southernmost Texas, where Aedes-borne viruses have been transmitted
locally in the past decade(Monaghan et al. 2016; Grubaugh et al.
2017), is at least moderate all-year-round. In California, where
Ae. aegypti was initially detected in 2013 and has since
spread(Pless et al. 2017), potential abundance is moderate across
large geographic areas of the state from July-October. Remarkably,
the simple MLR model produces nearly identical seasonal patterns of
potential abundance compared to estimates from complex dynamic
mosquito simulation models.(Monaghan et al. 2016) Comment by Ernst,
Kacey C - (kernst): Are all of these estimates using the Phoenix
specified model so it is comparing low, medium high to the ranges
identified in Phoenix? There is no reset to a max trap count for
each place, right?Comment by Ernst, Kacey C - (kernst): Can you do
a geographic overlay here and actually calculate an estimated
agreement?
Users of the MLR model should note several limitations. The
model was fit using data from an arid city that differs from humid
environments where Ae. aegypti is most common studied using traps
that are not specific to Ae. aegypti. Rainfall did not emerge as a
significant predictor, though it can be an important source of
water for the immature stages of Ae. aegypti.(Morin et al. 2015) It
is possible that vapor pressure, which is correlated with rainfall,
is an adequate proxy of rainfall in the MLR model. Temperatures in
Phoenix regularly exceed 40 oC in summer and are not typical of
many environments where Ae. aegypti is present(Eisen et al. 2014).
The use of minimum temperature in the MLR partially addresses this
difference, as the greater nighttime cooling in desert cities like
Phoenix can lead to average minimum temperatures comparable to
humid cities like Miami. The use of one-month meteorological
averages as predictors may limit the models ability to detect
fluctuations in Ae. aegypti populations related to episodic events
such as large storms or heatwaves. The model does not account for
the effects of interspecies competition(Reiskind and Lounibos
2013), adaptation(Kearney et al. 2009), winter egg survival(Brady
et al. 2014) or diel temperature fluctuations.(Lambrechts et al.
2011) Finally, the model neglects non-climatic factors important
for supporting Ae. aegypti such as presence of humans and
availability of container habitats.(Hayden et al. 2015) Comment by
Microsoft Office User: What you lose with this approach is the
ability to define a threshold or low, medium, high levels that may
have a biological or epidemiological relevance. I think this is ok,
but we need to be pretty explicit that we are saying, in comparison
to other time periods in Phoenix, instead of as compared to when
there is no risk of dengue transmission, etc.Andy: Note inherently
arbitray scales, yet they do make some sense based on Fig. 2
Despite its simplicity and limitations, the model produces
seasonal estimates of Ae. aegypti potential abundance across the
U.S. consistent with complex dynamic model simulations(Monaghan et
al. 2016) and presence records.(Hahn et al. 2016) Regions where
predicted Ae. aegypti potential abundance is moderate or high
nearly year-round coincide with areas of recent arbovirus
transmission.(Monaghan et al. 2016; Grubaugh et al. 2017) We
demonstrate that the model is useful for understanding the general
seasonality of Ae. aegypti mosquitoes in the U.S. The model was
designed for easy, rapid, low-cost implementation using readily
available meteorological data (see Supplemental Text 1 for
step-by-step instructions). It may be most beneficial for real time
applications, such as forecasting suitable conditions for Ae.
aegypti a month in advance for local action, including informing
timing of surveillance activities in areas without a current
program, triggering mobilization of vector control activities, and
initiation of educational campaigns to the public to prepare their
yards for the season. , in order to inform public health or
vector-control activities, or to guide household-level actions to
reduce breeding sites and human exposure.Comment by Ernst, Kacey C
- (kernst): Possible to conduct an analyses quantifying this?
Acknowledgements
We thank Rebecca Eisen and Tammi Johnson of CDC for providing
their habitat suitability model results.(Johnson et al. 2017)
Financial Support
This work was funded by NASA Grant NNX16AO98G. The National
Center for Atmospheric Research is sponsored by the National
Science Foundation.
References
1. Grubaugh ND et al., 2017. Genomic epidemiology reveals
multiple introductions of Zika virus into the United States. Nature
546: 401405.
2. Johnson TL et al., 2017. Modeling the Environmental
Suitability for Aedes (Stegomyia) aegypti and Aedes (Stegomyia)
albopictus (Diptera: Culicidae) in the Contiguous United States. J
Med Entomol tjx163.
3. Hahn MB, Eisen RJ, Eisen L, Boegler KA, Moore CG, McAllister
J, Savage HM, Mutebi J-P, 2016. Reported Distribution of Aedes (
Stegomyia ) aegypti and Aedes ( Stegomyia ) albopictus in the
United States, 1995-2016 (Diptera: Culicidae). J Med Entomol 53:
11691175.
4. Monaghan AJ et al., 2016. On the Seasonal Occurrence and
Abundance of the Zika Virus Vector Mosquito Aedes Aegypti in the
Contiguous United States. PLoS Curr 8:
ecurrents.outbreaks.50dfc7f46798675fc63e7d7da563da76.
5. Eisen L, Moore CG, 2013. Aedes (Stegomyia) aegypti in the
continental United States: a vector at the cool margin of its
geographic range. J Med Entomol 50: 467478.
6. Morin CW, Comrie AC, Ernst K, 2013. Climate and Dengue
Transmission: Evidence and Implications. Environ Health Perspect
121: 12641272.
7. Focks DA, Haile DG, Daniels E, Mount GA, 1993. Dynamic Life
Table Model for Aedes aegypti (Diptera: Culicidae): Analysis of the
Literature and Model Development. J Med Entomol 30: 10031017.
8. Morin CW, Monaghan AJ, Hayden MH, Barrera R, Ernst K, 2015.
Meteorologically Driven Simulations of Dengue Epidemics in San
Juan, PR. PLoS Negl Trop Dis 9: e0004002.
9. Xu C, Legros M, Gould F, Lloyd AL, 2010. Understanding
Uncertainties in Model-Based Predictions of Aedes aegypti
Population Dynamics. PLoS Negl Trop Dis 4: e830.
10. Reiskind MH, Lounibos LP, 2013. Spatial and temporal
patterns of abundance of Aedes aegypti L. (Stegomyia aegypti) and
Aedes albopictus (Skuse) [Stegomyia albopictus (Skuse)] in southern
Florida. Med Vet Entomol 27: 421429.
11. Sukumaran D, 2016. A review on use of attractants and traps
for host seeking Aedes aegypti mosquitoes. Indian J Nat Prod Resour
IJNPR Former Nat Prod Radiance NPR 7: 207214.
12. Cosgrove BA et al., 2003. Real-time and retrospective
forcing in the North American Land Data Assimilation System (NLDAS)
project. J Geophys Res Atmospheres 108: 8842.
13. Eisen L, Monaghan AJ, Lozano-Fuentes S, Steinhoff DF, Hayden
MH, Bieringer PE, 2014. The Impact of Temperature on the Bionomics
of Aedes (Stegomyia) Aegypti, with Special Reference to the Cool
Geographic Range Margins. J Med Entomol 51: 496516.
14. Pless E, Gloria-Soria A, Evans BR, Kramer V, Bolling BG,
Tabachnick WJ, Powell JR, 2017. Multiple introductions of the
dengue vector, Aedes aegypti, into California. PLoS Negl Trop Dis
11: e0005718.
15. Kearney M, Porter WP, Williams C, Ritchie S, Hoffmann AA,
2009. Integrating biophysical models and evolutionary theory to
predict climatic impacts on species ranges: the dengue mosquito
Aedes aegypti in Australia. Funct Ecol 23: 528538.
16. Brady OJ et al., 2014. Global temperature constraints on
Aedes aegypti and Ae. albopictus persistence and competence for
dengue virus transmission. Parasit Vectors 7: 338.
17. Lambrechts L, Paaijmans KP, Fansiri T, Carrington LB, Kramer
LD, Thomas MB, Scott TW, 2011. Impact of daily temperature
fluctuations on dengue virus transmission by Aedes aegypti. Proc
Natl Acad Sci 108: 74607465.
18. Hayden MH, Cavanaugh JL, Tittel C, Butterworth M, Haenchen
S, Dickinson K, Monaghan AJ, Ernst KC, 2015. Post Outbreak Review:
Dengue Preparedness and Response in Key West, Florida. Am J Trop
Med Hyg 93: 397400.
PHOENIX MIAMI
Figure 1. Phoenix (left) and Miami (right) observed vs.
predicted: a,d) log(aegypti) by year and month; b,e) multi-year
average log(aegypti) by month; and c,f) multi-year average
potential abundance categories by month. The multi-year average for
Phoenix spans January 2006-December 2015, and for Miami spans June
2006-June 2008. Potential abundance categories are 3 for high, 2
for medium and 1 for low.
Figure 2. 2006-2015 average monthly log(aegypti) in the U.S.,
predicted using the average meteorological fields from the prior
month per equation (1). For example, average August log(aegypti) is
based on average meteorological conditions in July. The
log(aegypti) values are color-coded into their respective potential
abundance categories including low (blue), medium (green) and high
(red).
Table 1. MLR model fit statistics.
Regression Statistics
Multiple R
0.86
R Square
0.74
Adjusted R Square
0.74
Standard Error
0.40
Observations
120
Coefficients
Standard Error
P-value
Lower 95%
Upper 95%
Intercept
-0.53968
0.097
0.000
-0.732
-0.348
Tmin
0.07041
0.007
0.000
0.056
0.085
e
0.03829
0.017
0.029
0.004
0.073
Supplemental Figure 1
Supplemental Figure 1. Scatter plots showing linear fits of
minimum temperature (top panel) and vapor pressure (bottom panel)
with log(aegypti) in Phoenix.
Supplemental Text 1
Step-by-step instructions on use of the MLR model to predict
monthly log(aegypti)
1. Compute the 30-day average daily minimum temperature (Tmin)
in units of oC for the previous 30 days (with lag of 3). For
example, if you are interested in making a prediction for the month
beginning on the 1st of July, you would compute the 30-day average
of Tmin from May 29 thru June 27. If you wanted to make a
prediction for the month beginning on the 8th of July, you would
average daily Tmin for June 5 thru July 4. Note that the
30-day-with-3-day-lag method of computing averages is designed
specifically for using NLDAS meteorological fields to make
predictions for the upcoming month in real time. For simplicity,
since monthly average meteorological fields are readily available,
you can achieve similar results using monthly averages as a proxy
of 30-day averages.
2. Compute the 30-day (or monthly) average vapor pressure, e, in
the same manner. Vapor pressure is an output of datasets such as
Daymet V3 and WorldClim V2. However, relative humidity is often a
more commonly available variable in climate datasets (e.g., NLDAS),
or directly from local weather stations. If you only have access to
relative humidity data, vapor pressure can be computed from monthly
relative humidity and average temperature data as follows (you can
skip to step 3 if you already have computed vapor pressure):
2a. Compute the 30-day (or monthly) average daily temperature
(Tavg) in the same manner.
2b. Compute the 30-day(or monthly) average daily relative
humidity (RH) in the same manner. (units=%)
2c. Compute the 30-day(or monthly) average vapor pressure (e),
in units of hPa, using Tavg and RH, as follows:
2c.1. es =6.11*10^((7.5*Tavg)/(237.3+Tavg))
2c.2. (es-e)
=1.33322*(((100-RH)/100)*4.9463*EXP(0.0621*Tavg))
2c.3. e = es - (es-e) ; i.e., subtract the result of 2c.2 from
the result of 2c.1
3. Compute log(aegypti) using the 30-day (or monthly) average
minimum temperature and vapor pressure using the following
equation. The value should be between 0-3 (and can occasionally
exceed 3 if conditions are highly suitable).
log(aegypti) =-0.53968+0.07041*Tmin+0.03829*e
For example, if the monthly average temperature is 25.6 oC and
the monthly average vapor pressure is 18.7 hPa: log(aegypti)
=-0.53968+0.07041*25.6+0.03829*18.7 = 1.98
4. Compute the Aedes aegypti potential abundance category:
High if log(aegypti) 2
Medium if log(aegypti) 1 and < 2
Low if log(aegypti) < 1
5. This monthly forecast can be updated at any desired time
interval (e.g., daily, weekly or monthly).
2
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y=0.0784x- 0.8614R=0.69882
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MinimumTemperaturevslog(aegypti)
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y=0.171x- 0.3694R=0.53341
00.51
1.52
2.53
3.5
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log(aegypti)
VaporPressure(hPa)
VaporPressurevslog(aegypti)
y=0.171x-0.3694
R=0.53341
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VaporPressurevslog(aegypti)
